Constructing Datasets for Multi-hop Reading Comprehension Across Documents

Understand

Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document.

• Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently there exist no resources to train and test this capability.
• We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods.
• In our task, a model learns to seek and combine evidence - effectively performing multi-hop (alias multi-step) inference.